Q1: Self-organizing map analysis of thermal conductivity dataset
The following code SOM-HexagonalTopology.py apply SOM unsupervised clustering to our thermal conductivity dataset and shows the cluster map, and maps the data samples to each of the clusters with colors showing its 1 out of 10 grades corresponding to their percentiles.
- (1) Add your code from line 99, so that your SOM map can show different colors for samples of different thermal conductivity grade (from 0 to 9, corresponding to their percentile).
Hint: this function from scipy which can calculate the percentile of a value in a list of numbers
from scipy import stats
print(stats.percentileofscore(target, 500))You need to install the minisom by: pip3 install minisom https://github.com/JustGlowing/minisom more info.
- (2) If possible, try to fix the legend bar so that the color show the range of thermal conductivity values. (optional, bonus points: 10)
Q2: Genetic programming for symbolic regression Study this fastsr symbolic regression package
https://github.com/cfusting/fast-symbolic-regression
read the thermal_dataset.csv file, use all the numeric columns except the y-exp and y-theory columns as the X_train, use the y-exp as the y_train
train a symbolic regression model for this dataset
print out the final regression score
print out the formula of the best individual plot the final regression scatter plot.